ML Anthology

Spectrum-Based Fault Localisation for Multi-Agent Systems

Lúcio S. Passos, Rui Abreu, Rosaldo J. F. Rossetti
IJCAI 2015 pp. 1134-1140
/ijcai/2015/passos2015ijcai-spectrum/

Abstract

Diagnosing unwanted behaviour in Multi-Agent Systems (MASs) is crucial to ascertain agents' correct operation. However, generation of MAS models is both error-prone and time intense, as it exponentially increases with the number of agents and their interactions. In this paper, we propose a light-weight, automatic debugging-based technique, coined ESFL-MAS, which shortens the diagnostic process, while only relying on minimal information about the system. ESFL-MAS uses a heuristic that quantifies the suspiciousness of an agent to be faulty; therefore, different heuristics may have different impact on the diagnostic quality. Our experimental evaluation shows that 10 out of 42 heuristics yield the best diagnostic accuracy (96.26% on average).

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Cite

Text

Passos et al. "Spectrum-Based Fault Localisation for Multi-Agent Systems." International Joint Conference on Artificial Intelligence, 2015.

Markdown

[Passos et al. "Spectrum-Based Fault Localisation for Multi-Agent Systems." International Joint Conference on Artificial Intelligence, 2015.](https://mlanthology.org/ijcai/2015/passos2015ijcai-spectrum/)

BibTeX

@inproceedings{passos2015ijcai-spectrum,
  title     = {{Spectrum-Based Fault Localisation for Multi-Agent Systems}},
  author    = {Passos, Lúcio S. and Abreu, Rui and Rossetti, Rosaldo J. F.},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2015},
  pages     = {1134-1140},
  url       = {https://mlanthology.org/ijcai/2015/passos2015ijcai-spectrum/}
}